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DESIGN OF A GENERIC PATH PATH PLANNING SYSTEM

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Wall following, Space filling curves, Splines,Topological maps, etc. ... Homogeneous, Heterogeneous. AILAB Path Planning Workgroup. 13. Characteristics of Domain ... – PowerPoint PPT presentation

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Title: DESIGN OF A GENERIC PATH PATH PLANNING SYSTEM


1
DESIGN OF A GENERIC PATH PATH PLANNING SYSTEM
  • AILAB
  • Path Planning Workgroup

2
OUTLINE
  • Path Planning Basics
  • Current Implementations
  • System Design
  • Conclusion

3
PATH PLANNING BASICS
  • Path
  • Configuration
  • Work Space
  • Configuration Space (Cspace)
  • Cell Decomposition
  • Roadmap (Skeletonization)
  • Free, Obstacle, Unknown Space
  • Dimension and Degrees of Freedom

4
Cell Decomposition
  • Regular Grids
  • Multiresolution Cells
  • Trapezoidal Cells

5
Roadmap (Skeletonization)
  • Meadow Maps
  • Generalized Voronoi Diagrams
  • Visibility Graphs
  • Probabilistic Roadmaps

6
Properties of Path Planners
  • Dynamic vs. static
  • Global vs. local
  • Optimal vs. suboptimal
  • Complete vs. heuristic
  • Metric vs. topological

7
Classification of Obstacles
  • Category of Obstacles from Arai et. al. Arai89,
    28

8
Path Planning Techniques
  • Reactive Methods
  • Artificial Potential Fields
  • Vector Field Histogram Method
  • Graph Traversing Methods
  • A Algorithm
  • Best First / Breadth First / Greedy Search
  • Wavefront Method
  • Other Methods
  • Wall following, Space filling curves,
    Splines,Topological maps, etc.

9
Problems with MA-PP
  • Possible problems of applying ordinary PP methods
    to MAS are,
  • Collisions,
  • Deadlock situations, etc.
  • Problems with MA-PP are,
  • Computational overhead,
  • Information exchange,
  • Communication overhead, etc.

10
Approaches
  • Cenralised All robots in one composite system.
  • Find complete and optimum solution if exists.
  • Use complete information
  • - Exponential computational complexity w.r.t of
    robots
  • - Single point of failure
  • Decoupled First generate paths for robots
    (independently), then handle interactions.
  • Proportional computation time w.r.t of robots
  • Robust
  • - Not complete
  • - Deadlocks may occur

11
Improvements for MA-PP
  • Priority assignment
  • Aging
  • Rule-Based methods
  • Resource allocation
  • Robot Groups
  • Virtual dampers and virtual springs
  • Assigning dynamic information to edges and
    vertices
  • ...

12
Characteristics of MAS
  • According to Dudek et. al. Dudek96,53,
  • Team Size 1, 2, limited, infinite
  • Communication Range None, Near, Infinite
  • Communication Topology Broadcast, Addressed,
    Tree, Graph
  • Communication Bandwidth High, Motion related,
    Low, Zero
  • Team Composition Homogeneous, Heterogeneous

13
Characteristics of Domain
  • Initial Information None, Partial, Complete
  • Number of Targets 1, Many
  • Target Available True (i.e. go to target), False
    (i.e. explore for target)
  • Stationary Targets True, False

14
Complexity of Path Planning
  • In 3D work space finding exact solution is
    NP-HARD. Xavier92, 54
  • Path planning is PSPACE-HARD. Reif79,55
  • The compexity increases exponentially with,
  • Number of DOF Canny88, 9
  • Number of agents

15
Imperfect solutions
  • Used in case of compex problems,
  • Approximation
  • Probabilistic
  • Heuristic
  • Special cases

16
CURRENT IMPLEMENTATIONS
  • Sampling Based Algorithms
  • Incomplete, but efficient and practical
  • Types
  • Multiple Query
  • Single Query

17
Multiple Query
  • A map is generated for multiple queries
  • Fill the space adequately
  • Probabilistic Roadmap
  • Uniform sampling of C-free
  • Local planner attempts connections
  • Biased sampling

18
Single Query
  • Suited for high dimensions
  • Find a path as quick as possible
  • RRTs
  • Grow from an initial state
  • RRT-Connect Grow from both initial and goal
  • Expand by performing incremental motions

19
Demos
  • Path Planning
  • Probabilistic Roadmap (PRM)
  • Different sampling methods
  • Rapidly-exploring Random Trees (RRTs)
  • RRT
  • RRT-Connect

20
SYSTEM DESIGN
  • Following slides are based on Lavelles Motion
    Strategy Library, implemented in C

21
Overview
  • MODULES
  • Model
  • Geom
  • Problem
  • Solver
  • Scene
  • Render
  • Gui

22
Model
  • Contain incremental simulators that model the
    kinematics and dynamics of a variety of
    mechanical systems. The methods allow planning
    algorithms to compute the future system state,
    given the current state, an interval of time, and
    a control input applied over that interval.

23
Geom
  • These define the geometric representations of all
    obstacles in the world, and of each part of the
    robot. The methods allow planning algorithms to
    determine whether any of the robot parts are in
    collision with each other or with obstacles in
    the world. (PQP - the Proximity Query Package )

24
Problem
  • This is an interface class to a planner, which
    abstracts the designer of a planning algorithm
    away from particular details such as collision
    detection, and dynamical simulations. Each
    instance of a problem includes both an instance
    of Model and of Geometry. An initial state and
    final state are also included, which leads to a
    problem to be solved by a solver (typically a
    planning algorithm).

25
Planner
  • The most important module.
  • Base for all path planners...

26
CONCLUSION
  • Path planning is a challenging task with many
    different applications.
  • Each application may device its own path planning
    strategy.
  • A generic path planning library may provide
    solution or guidelines for other path planners.
  • ...

27
QUESTIONS?
  • Thank you...
  • kaplanke_at_boun.edu.tr
  • fuatgeleri_at_gmail.com
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